Simulating the Heston Model with Python | Stochastic Volatility Modelling
The Heston model is a useful model for simulating stochastic volatility and its effect on the potential paths an asset can take over the life of an option. It's popular because of:
easy closed-form solution for European option pricing
no risk of negative variances
incorporation of leverage effect
This allows for more effective modeling than the Black-Scholes formula allows due to its restrictive assumption of constant volatility.
One of the nice things about the Heston model for European option prices is that there is a closed-form solution once you have the characteristic function. So, discretisation of the SDE is not required for valuing a European option, however if you would like to value other option types with complex features using the Heston model than you can use the following code.
Written Tutorial on Medium: / simulating-the-heston-model-in-python
★ ★ Code Available on GitHub ★ ★
GitHub: https://github.com/TheQuantPy
Specific Tutorial Link: https://github.com/TheQuantPy/youtube...
Great resource for explanation here in how to complete the Euler Discretization:
Euler and Milstein Discretization by Fabrice Douglas Rouah https://frouah.com/finance%20notes/Eu...
00:00 Intro
00:53 Heston Model Dynamics
02:15 Monte Carlo Simulation and SDE Discretization
05:03 Heston Model Simulation in Python
10:00 Visualising the asset price density and volatility smile
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